Canonical correlation analysis-based fault detection methods with application to alumina evaporation process

被引：177

作者：

Chen, Zhiwen ^{[1
]}

Ding, Steven X. ^{[1
]}

Zhang, Kai ^{[1
]}

Li, Zhebin ^{[2
]}

Hu, Zhikun ^{[2
]}

机构：

[1] Univ Duisburg Essen, Inst Automat Control & Complex Syst, D-47057 Duisburg, Germany

[2] Cent S Univ, Sch Phys & Elect, Changsha 410083, Peoples R China

来源：

CONTROL ENGINEERING PRACTICE | 2016年 / 46卷

基金：

中国国家自然科学基金;

关键词：

Canonical correlation analysis; Residual generation; Fault detection; Alumina evaporation process; MODEL-BASED DIAGNOSIS; HOT STRIP MILL; SUBSPACE IDENTIFICATION; NONLINEAR PROCESSES; KERNEL PCA; SYSTEMS; SCHEME; REALIZATION; DESIGN;

D O I：

10.1016/j.conengprac.2015.10.006

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

080201 [机械制造及其自动化];

摘要：

In this paper, canonical correlation analysis (CCA)-based fault detection methods are proposed for both static and dynamic processes. Different from the well-established process monitoring and fault diagnosis systems based on multivariate analysis techniques like principal component analysis and partial least squares, the core of the proposed methods is to build residual signals by means of the CCA technique for the fault detection purpose. The proposed methods are applied to an alumina evaporation process, and the achieved results show that both methods are applicable for fault detection, while the dynamic one delivers better detection performance. (C) 2015 Published by Elsevier Ltd.

引用

页码：51 / 58

页数：8

共 45 条

[1]

STOCHASTIC THEORY OF MINIMAL REALIZATION [J].